Project Grant 2530786
- This $500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE, CFDA 47.070) program supports research by the Regents of the University of California at Riverside to study security vulnerabilities in machine learning (ML) models. The project aims to understand how malicious actors could exploit unused parameters in trained ML models to install additional, potentially harmful functionality without detection. The research...
- This Project Grant award, provided by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), aims to improve the security and resilience of machine learning (ML) software. The $262,828 award, issued on March 15, 2025, will support the development of methods for detecting and correcting non-functional vulnerabilities in ML libraries, such as denial-of-service attacks and side-channel attacks. This research...
- This National Science Foundation project grant of $599,999 will fund research at Duke University from October 2022 through September 2026 towards developing secure methods for federated learning. Federated learning is an emerging machine learning technique that allows analysis of private data without centralized collection, but current methods lack security protections. Under the Computer and Information Science and Engineering program (CFDA 47.070), the researchers will explore new security...
- This federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, supports research to develop improved methods for assessing privacy risks in machine learning (ML) models trained on sensitive tabular data such as patient records or financial information. The $379,224 award to The Pennsylvania State University aims to create frameworks for auditing attribute inference risks and disparities in both...
- This four-year $300,000 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program aims to develop secure foundations for federated learning. Federated learning enables machine learning models to be collaboratively trained using data from many client devices without sharing private information. The researchers will investigate security vulnerabilities in federated learning's training phase, such as poisoning and backdoor attacks. They will...
- This Project Grant award of $246,516 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports research to enhance the security and robustness of machine learning (ML) systems in multi-tenant cloud FPGA (field programmable gate array) environments. The project aims to: (1) understand the vulnerabilities of ML cloud-FPGA systems and explore defensive approaches; (2) advance the security of ML cloud systems against hardware-based model...
- The National Science Foundation awarded a $721,000 project grant to the University of Arizona under the Computer and Information Science and Engineering federal grant program (CFDA 47.070) to develop a new software platform called DEEPSECURE. DEEPSECURE will integrate essential functions and building blocks to support privacy-preserving and secure machine learning research. It will include a scalable and customizable modular framework with seamlessly integrated libraries, function blocks, and...
- This $180,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research project to secure large language models (LLMs) against prompt injection attacks. The project aims to: (1) conduct a systematic study to deepen understanding of these security threats, and (2) develop new defenses to mitigate such attacks. The research will establish foundational security principles for the rapidly growing...
- This $300,000 Project Grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA #47.070) program aims to strengthen national cybersecurity and artificial intelligence education efforts. The project will build a comprehensive collection of hands-on labs that simulate real-world security challenges through cloud-based virtual environments. These labs are designed to integrate into cybersecurity, data science, and broader computing...
- This National Science Foundation (NSF) Project Grant award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $391,848 to Purdue University to develop holistic systems for securing the machine learning supply chain. The project aims to create tools to quantify trust in machine learning supply chains and verify security requirements across those supply chains. The research will also support the development of a diverse next generation of computer...
This Project Grant award from the National Science Foundation's Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $600,000 to Florida State University (FSU) to develop a security-focused framework to protect collaborative scientific computing in Machine Learning as a Service (MLaaS) environments. The key products and services to be delivered include: The award period runs from January 1, 2026 to December 31, 2028, with the goal of creating secure infrastructure to support scientific collaborations in sensitive domains like medicine, genomics, and disaster response without compromising data trust and safety.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $600.0k | 7/15/25 |